Search Results for author: Yuanda Wang

Found 7 papers, 2 papers with code

Protecting Activity Sensing Data Privacy Using Hierarchical Information Dissociation

no code implementations4 Sep 2024 Guangjing Wang, Hanqing Guo, Yuanda Wang, Bocheng Chen, Ce Zhou, Qiben Yan

Hippo achieves fine-grained control over the disclosure of sensitive information without requiring private labels.

The Dark Side of Human Feedback: Poisoning Large Language Models via User Inputs

no code implementations1 Sep 2024 Bocheng Chen, Hanqing Guo, Guangjing Wang, Yuanda Wang, Qiben Yan

Large Language Models (LLMs) have demonstrated great capabilities in natural language understanding and generation, largely attributed to the intricate alignment process using human feedback.

Language Modelling Natural Language Understanding

ViC: Virtual Compiler Is All You Need For Assembly Code Search

1 code implementation10 Aug 2024 Zeyu Gao, Hao Wang, Yuanda Wang, Chao Zhang

Assembly code search is vital for reducing the burden on reverse engineers, allowing them to quickly identify specific functions using natural language within vast binary programs.

Code Search Language Modelling +1

XuanCe: A Comprehensive and Unified Deep Reinforcement Learning Library

1 code implementation25 Dec 2023 Wenzhang Liu, Wenzhe Cai, Kun Jiang, Guangran Cheng, Yuanda Wang, Jiawei Wang, Jingyu Cao, Lele Xu, Chaoxu Mu, Changyin Sun

In this paper, we present XuanCe, a comprehensive and unified deep reinforcement learning (DRL) library designed to be compatible with PyTorch, TensorFlow, and MindSpore.

reinforcement-learning

Beyond Boundaries: A Comprehensive Survey of Transferable Attacks on AI Systems

no code implementations20 Nov 2023 Guangjing Wang, Ce Zhou, Yuanda Wang, Bocheng Chen, Hanqing Guo, Qiben Yan

This survey offers a holistic understanding of the prevailing transferable attacks and their impacts across different domains.

Autonomous Driving Data Poisoning +2

PhantomSound: Black-Box, Query-Efficient Audio Adversarial Attack via Split-Second Phoneme Injection

no code implementations13 Sep 2023 Hanqing Guo, Guangjing Wang, Yuanda Wang, Bocheng Chen, Qiben Yan, Li Xiao

We significantly enhance the query efficiency and reduce the cost of a successful untargeted and targeted adversarial attack by 93. 1% and 65. 5% compared with the state-of-the-art black-box attacks, using merely ~300 queries (~5 minutes) and ~1, 500 queries (~25 minutes), respectively.

Adversarial Attack

Cannot find the paper you are looking for? You can Submit a new open access paper.